Business decisions made from a cohesive view of a business enterprise are critically important to ensure the enterprise is operating at its most efficient levels. Just as human body systems take input from their senses in order to automatically regulate heart rate, oxygen level, and blood flow to fit an appropriate need without requiring conscience decisions, automated business decision-making should occur when data is “fused” together. The fused data should form a view of a business situation and should be used to apply knowledge with respect to appropriate actions that are necessary for a given business system/environment.
A “data fusion” approach differs from conventional “automation” approaches to decision making in that there is not a clear relationship between the data elements and any specific business rules (e.g., one can't simply take a data element and compare it to established thresholds to accurately determine the applicability of a business rule). With data fusion multiple data elements are collectively analyzed, before a decision is made. The individual data elements themselves may not have significant meaning alone and, in fact, may appear unrelated; however, when joined together with other data elements and/or through applied analytics, the data elements can create a context of what is actually happening and thus provide a more accurate view of a given business situation that needs to be addressed. Yet, existing business decision-making techniques do not use data fusion.
With existing techniques, data elements are used to directly trigger business rules. These approaches are linear, rigid, and often hard coded into business systems for the enterprise. As a result, new business situations are often not detected with present techniques, since any new situation must be hard coded or otherwise accounted for within the business systems before it can be detected.
Moreover, if data elements appear to be related, then conventional approaches will often empirically adjust the business rules within the systems to account for these relationships. One of ordinary skill in the art readily appreciates, that hard coded adjustments to the business systems is not a preferred choice of the enterprise, since often business conditions are dynamically and rapidly changing, and there is a delay before these changed circumstances can be adequately represented in the business systems. During this delay, new situations can also occur that further complicate and delay an enterprise's attempt to provide a robust, timely, and efficient business decision-making system.
Therefore, there exist needs for providing techniques, methods, and systems that use data fusion techniques to automate business decision-making. With such techniques, methods, and systems, enterprises can more timely and efficiently react and adjust to their environment and market.